The Lundberg Laboratory for Diabetes Research, Department of Molecular and Clinical Medicine, the Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden.
Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-17121, Sweden.
Sci Rep. 2020 Jun 19;10(1):9973. doi: 10.1038/s41598-020-66199-z.
We evaluated the importance of body composition, amount of subcutaneous and visceral fat, liver and heart ectopic fat, adipose tissue distribution and cell size as predictors of cardio-metabolic risk in 53 non-obese male individuals. Known family history of type 2 diabetes was identified in 25 individuals. The participants also underwent extensive phenotyping together with measuring different biomarkers and non-targeted serum metabolomics. We used ensemble learning and other machine learning approaches to identify predictors with considerable relative importance and their intricate interactions. Visceral fat and age were strong individual predictors of ectopic fat accumulation in liver and heart along with markers of lipid oxidation and reduced glucose tolerance. Subcutaneous adipose cell size was the strongest individual predictor of whole-body insulin sensitivity and also a marker of visceral and ectopic fat accumulation. The metabolite 3-MOB along with related branched-chain amino acids demonstrated strong predictability for family history of type 2 diabetes.
我们评估了 53 名非肥胖男性个体的身体成分、皮下和内脏脂肪量、肝和心脏异位脂肪、脂肪组织分布和细胞大小作为心血管代谢风险预测因子的重要性。25 名个体有 2 型糖尿病家族史。参与者还进行了广泛的表型分析，同时测量了不同的生物标志物和非靶向血清代谢组学。我们使用集成学习和其他机器学习方法来识别具有相当相对重要性的预测因子及其复杂的相互作用。内脏脂肪和年龄是肝和心脏异位脂肪积累以及脂质氧化和葡萄糖耐量降低标志物的强个体预测因子。皮下脂肪细胞大小是全身胰岛素敏感性的最强个体预测因子，也是内脏和异位脂肪积累的标志物。代谢物 3-MOB 及其相关支链氨基酸对 2 型糖尿病家族史具有很强的预测能力。
